Overview

Dataset statistics

Number of variables23
Number of observations145460
Missing cells343248
Missing cells (%)10.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.5 MiB
Average record size in memory184.0 B

Variable types

NUM16
CAT5
BOOL2

Warnings

Date has a high cardinality: 3436 distinct values High cardinality
Pressure3pm is highly correlated with Pressure9amHigh correlation
Pressure9am is highly correlated with Pressure3pmHigh correlation
Temp9am is highly correlated with MinTempHigh correlation
MinTemp is highly correlated with Temp9amHigh correlation
Temp3pm is highly correlated with MaxTempHigh correlation
MaxTemp is highly correlated with Temp3pmHigh correlation
MinTemp has 1485 (1.0%) missing values Missing
Rainfall has 3261 (2.2%) missing values Missing
Evaporation has 62790 (43.2%) missing values Missing
Sunshine has 69835 (48.0%) missing values Missing
WindGustDir has 10326 (7.1%) missing values Missing
WindGustSpeed has 10263 (7.1%) missing values Missing
WindDir9am has 10566 (7.3%) missing values Missing
WindDir3pm has 4228 (2.9%) missing values Missing
WindSpeed9am has 1767 (1.2%) missing values Missing
WindSpeed3pm has 3062 (2.1%) missing values Missing
Humidity9am has 2654 (1.8%) missing values Missing
Humidity3pm has 4507 (3.1%) missing values Missing
Pressure9am has 15065 (10.4%) missing values Missing
Pressure3pm has 15028 (10.3%) missing values Missing
Cloud9am has 55888 (38.4%) missing values Missing
Cloud3pm has 59358 (40.8%) missing values Missing
Temp9am has 1767 (1.2%) missing values Missing
Temp3pm has 3609 (2.5%) missing values Missing
RainToday has 3261 (2.2%) missing values Missing
RainTomorrow has 3267 (2.2%) missing values Missing
Rainfall has 91080 (62.6%) zeros Zeros
Sunshine has 2359 (1.6%) zeros Zeros
WindSpeed9am has 8745 (6.0%) zeros Zeros
Cloud9am has 8642 (5.9%) zeros Zeros
Cloud3pm has 4974 (3.4%) zeros Zeros

Reproduction

Analysis started2022-12-26 11:12:34.397783
Analysis finished2022-12-26 11:13:43.416758
Duration1 minute and 9.02 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Date
Categorical

HIGH CARDINALITY

Distinct3436
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2013-11-12
 
49
2014-09-01
 
49
2014-08-23
 
49
2014-08-24
 
49
2014-08-25
 
49
Other values (3431)
145215 
ValueCountFrequency (%) 
2013-11-1249< 0.1%
 
2014-09-0149< 0.1%
 
2014-08-2349< 0.1%
 
2014-08-2449< 0.1%
 
2014-08-2549< 0.1%
 
2014-08-2649< 0.1%
 
2014-08-2749< 0.1%
 
2014-08-2849< 0.1%
 
2014-08-2949< 0.1%
 
2014-08-3049< 0.1%
 
Other values (3426)14497099.7%
 
2022-12-26T16:13:43.592344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique92 ?
Unique (%)0.1%
2022-12-26T16:13:43.741832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

Location
Categorical

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Canberra
 
3436
Sydney
 
3344
Darwin
 
3193
Melbourne
 
3193
Brisbane
 
3193
Other values (44)
129101 
ValueCountFrequency (%) 
Canberra34362.4%
 
Sydney33442.3%
 
Darwin31932.2%
 
Melbourne31932.2%
 
Brisbane31932.2%
 
Adelaide31932.2%
 
Perth31932.2%
 
Hobart31932.2%
 
Albany30402.1%
 
MountGambier30402.1%
 
Other values (39)11344278.0%
 
2022-12-26T16:13:43.899043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-12-26T16:13:44.071293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length16
Median length8
Mean length8.711625189
Min length4

MinTemp
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct389
Distinct (%)0.3%
Missing1485
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean12.19403438
Minimum-8.5
Maximum33.9
Zeros159
Zeros (%)0.1%
Memory size1.1 MiB
2022-12-26T16:13:44.212690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-8.5
5-th percentile1.8
Q17.6
median12
Q316.9
95-th percentile23
Maximum33.9
Range42.4
Interquartile range (IQR)9.3

Descriptive statistics

Standard deviation6.398494976
Coefficient of variation (CV)0.5247233832
Kurtosis-0.483972117
Mean12.19403438
Median Absolute Deviation (MAD)4.6
Skewness0.02118828401
Sum1755636.1
Variance40.94073795
MonotocityNot monotonic
2022-12-26T16:13:44.369308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
118990.6%
 
10.28980.6%
 
9.68960.6%
 
10.58840.6%
 
10.88720.6%
 
98720.6%
 
108710.6%
 
128660.6%
 
8.98610.6%
 
10.48600.6%
 
Other values (379)13519692.9%
 
(Missing)14851.0%
 
ValueCountFrequency (%) 
-8.51< 0.1%
 
-8.22< 0.1%
 
-82< 0.1%
 
-7.81< 0.1%
 
-7.62< 0.1%
 
ValueCountFrequency (%) 
33.91< 0.1%
 
31.91< 0.1%
 
31.81< 0.1%
 
31.43< 0.1%
 
31.21< 0.1%
 

MaxTemp
Real number (ℝ)

HIGH CORRELATION

Distinct505
Distinct (%)0.4%
Missing1261
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean23.22134828
Minimum-4.8
Maximum48.1
Zeros14
Zeros (%)< 0.1%
Memory size1.1 MiB
2022-12-26T16:13:44.526332image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-4.8
5-th percentile12.8
Q117.9
median22.6
Q328.2
95-th percentile35.5
Maximum48.1
Range52.9
Interquartile range (IQR)10.3

Descriptive statistics

Standard deviation7.119048846
Coefficient of variation (CV)0.3065734496
Kurtosis-0.2246297848
Mean23.22134828
Median Absolute Deviation (MAD)5.1
Skewness0.2208393481
Sum3348495.2
Variance50.68085647
MonotocityNot monotonic
2022-12-26T16:13:44.698977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
208850.6%
 
198430.6%
 
19.88400.6%
 
20.48340.6%
 
19.98230.6%
 
20.88170.6%
 
19.58120.6%
 
18.58110.6%
 
218100.6%
 
18.28040.6%
 
Other values (495)13592093.4%
 
(Missing)12610.9%
 
ValueCountFrequency (%) 
-4.81< 0.1%
 
-4.11< 0.1%
 
-3.81< 0.1%
 
-3.71< 0.1%
 
-3.21< 0.1%
 
ValueCountFrequency (%) 
48.11< 0.1%
 
47.32< 0.1%
 
471< 0.1%
 
46.91< 0.1%
 
46.83< 0.1%
 

Rainfall
Real number (ℝ≥0)

MISSING
ZEROS

Distinct681
Distinct (%)0.5%
Missing3261
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean2.36091815
Minimum0
Maximum371
Zeros91080
Zeros (%)62.6%
Memory size1.1 MiB
2022-12-26T16:13:45.137915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.8
95-th percentile13
Maximum371
Range371
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation8.478059738
Coefficient of variation (CV)3.591001127
Kurtosis178.1520788
Mean2.36091815
Median Absolute Deviation (MAD)0
Skewness9.83622525
Sum335720.2
Variance71.87749692
MonotocityNot monotonic
2022-12-26T16:13:45.295111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
09108062.6%
 
0.287616.0%
 
0.437822.6%
 
0.625921.8%
 
0.820561.4%
 
117591.2%
 
1.215351.1%
 
1.413770.9%
 
1.612000.8%
 
1.811040.8%
 
Other values (671)2695318.5%
 
(Missing)32612.2%
 
ValueCountFrequency (%) 
09108062.6%
 
0.11570.1%
 
0.287616.0%
 
0.365< 0.1%
 
0.437822.6%
 
ValueCountFrequency (%) 
3711< 0.1%
 
367.61< 0.1%
 
278.41< 0.1%
 
268.61< 0.1%
 
247.21< 0.1%
 

Evaporation
Real number (ℝ≥0)

MISSING

Distinct358
Distinct (%)0.4%
Missing62790
Missing (%)43.2%
Infinite0
Infinite (%)0.0%
Mean5.468231523
Minimum0
Maximum145
Zeros244
Zeros (%)0.2%
Memory size1.1 MiB
2022-12-26T16:13:45.451886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12.6
median4.8
Q37.4
95-th percentile12
Maximum145
Range145
Interquartile range (IQR)4.8

Descriptive statistics

Standard deviation4.193704094
Coefficient of variation (CV)0.7669214583
Kurtosis45.0432665
Mean5.468231523
Median Absolute Deviation (MAD)2.4
Skewness3.761286011
Sum452058.7
Variance17.58715403
MonotocityNot monotonic
2022-12-26T16:13:45.609108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
433392.3%
 
826091.8%
 
2.220951.4%
 
220321.4%
 
2.620031.4%
 
2.420031.4%
 
1.819791.4%
 
319731.4%
 
3.419671.4%
 
3.219561.3%
 
Other values (348)6071441.7%
 
(Missing)6279043.2%
 
ValueCountFrequency (%) 
02440.2%
 
0.18< 0.1%
 
0.25030.3%
 
0.310< 0.1%
 
0.47690.5%
 
ValueCountFrequency (%) 
1451< 0.1%
 
86.21< 0.1%
 
82.41< 0.1%
 
81.21< 0.1%
 
77.31< 0.1%
 

Sunshine
Real number (ℝ≥0)

MISSING
ZEROS

Distinct145
Distinct (%)0.2%
Missing69835
Missing (%)48.0%
Infinite0
Infinite (%)0.0%
Mean7.611177521
Minimum0
Maximum14.5
Zeros2359
Zeros (%)1.6%
Memory size1.1 MiB
2022-12-26T16:13:45.765893image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q14.8
median8.4
Q310.6
95-th percentile12.8
Maximum14.5
Range14.5
Interquartile range (IQR)5.8

Descriptive statistics

Standard deviation3.785482965
Coefficient of variation (CV)0.4973583857
Kurtosis-0.8294593402
Mean7.611177521
Median Absolute Deviation (MAD)2.6
Skewness-0.4964800381
Sum575595.3
Variance14.32988128
MonotocityNot monotonic
2022-12-26T16:13:45.923109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
023591.6%
 
10.711010.8%
 
1110940.8%
 
10.810690.7%
 
10.510270.7%
 
10.910210.7%
 
10.310100.7%
 
10.29930.7%
 
109840.7%
 
11.19780.7%
 
Other values (135)6398944.0%
 
(Missing)6983548.0%
 
ValueCountFrequency (%) 
023591.6%
 
0.15420.4%
 
0.25210.4%
 
0.34330.3%
 
0.43260.2%
 
ValueCountFrequency (%) 
14.51< 0.1%
 
14.34< 0.1%
 
14.22< 0.1%
 
14.16< 0.1%
 
1415< 0.1%
 

WindGustDir
Categorical

MISSING

Distinct16
Distinct (%)< 0.1%
Missing10326
Missing (%)7.1%
Memory size1.1 MiB
W
9915 
SE
9418 
N
9313 
SSE
9216 
E
9181 
Other values (11)
88091 
ValueCountFrequency (%) 
W99156.8%
 
SE94186.5%
 
N93136.4%
 
SSE92166.3%
 
E91816.3%
 
S91686.3%
 
WSW90696.2%
 
SW89676.2%
 
SSW87366.0%
 
WNW82525.7%
 
Other values (6)4389930.2%
 
(Missing)103267.1%
 
2022-12-26T16:13:46.079777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-12-26T16:13:46.205197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length2.252069297
Min length1

WindGustSpeed
Real number (ℝ≥0)

MISSING

Distinct67
Distinct (%)< 0.1%
Missing10263
Missing (%)7.1%
Infinite0
Infinite (%)0.0%
Mean40.03523007
Minimum6
Maximum135
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2022-12-26T16:13:46.361970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile20
Q131
median39
Q348
95-th percentile65
Maximum135
Range129
Interquartile range (IQR)17

Descriptive statistics

Standard deviation13.60706227
Coefficient of variation (CV)0.3398772092
Kurtosis1.41864232
Mean40.03523007
Median Absolute Deviation (MAD)9
Skewness0.874878878
Sum5412643
Variance185.1521435
MonotocityNot monotonic
2022-12-26T16:13:46.519227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3592156.3%
 
3987946.0%
 
3184285.8%
 
3780475.5%
 
3379335.5%
 
4173695.1%
 
3070384.8%
 
4366094.5%
 
2864784.5%
 
4454323.7%
 
Other values (57)5985441.1%
 
(Missing)102637.1%
 
ValueCountFrequency (%) 
61< 0.1%
 
719< 0.1%
 
9910.1%
 
111920.1%
 
135320.4%
 
ValueCountFrequency (%) 
1353< 0.1%
 
1301< 0.1%
 
1262< 0.1%
 
1242< 0.1%
 
1223< 0.1%
 

WindDir9am
Categorical

MISSING

Distinct16
Distinct (%)< 0.1%
Missing10566
Missing (%)7.3%
Memory size1.1 MiB
N
11758 
SE
9287 
E
9176 
SSE
9112 
NW
8749 
Other values (11)
86812 
ValueCountFrequency (%) 
N117588.1%
 
SE92876.4%
 
E91766.3%
 
SSE91126.3%
 
NW87496.0%
 
S86596.0%
 
W84595.8%
 
SW84235.8%
 
NNE81295.6%
 
NNW79805.5%
 
Other values (6)4516231.0%
 
(Missing)105667.3%
 
2022-12-26T16:13:46.675959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-12-26T16:13:46.801935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length2.242169669
Min length1

WindDir3pm
Categorical

MISSING

Distinct16
Distinct (%)< 0.1%
Missing4228
Missing (%)2.9%
Memory size1.1 MiB
SE
10838 
W
10110 
S
9926 
WSW
9518 
SSE
9399 
Other values (11)
91441 
ValueCountFrequency (%) 
SE108387.5%
 
W101107.0%
 
S99266.8%
 
WSW95186.5%
 
SSE93996.5%
 
SW93546.4%
 
N88906.1%
 
WNW88746.1%
 
NW86105.9%
 
ESE85055.8%
 
Other values (6)4720832.5%
 
2022-12-26T16:13:46.927462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-12-26T16:13:47.052951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length2.230984463
Min length1

WindSpeed9am
Real number (ℝ≥0)

MISSING
ZEROS

Distinct43
Distinct (%)< 0.1%
Missing1767
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean14.04342591
Minimum0
Maximum130
Zeros8745
Zeros (%)6.0%
Memory size1.1 MiB
2022-12-26T16:13:47.194054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median13
Q319
95-th percentile30
Maximum130
Range130
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.915375323
Coefficient of variation (CV)0.6348433336
Kurtosis1.226990907
Mean14.04342591
Median Absolute Deviation (MAD)6
Skewness0.7776295123
Sum2017942
Variance79.48391714
MonotocityNot monotonic
2022-12-26T16:13:47.335610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%) 
9136499.4%
 
13131329.0%
 
11117288.1%
 
17107887.4%
 
7107837.4%
 
15106257.3%
 
691186.3%
 
1987636.0%
 
087456.0%
 
2080635.5%
 
Other values (33)3829926.3%
 
ValueCountFrequency (%) 
087456.0%
 
246093.2%
 
463604.4%
 
691186.3%
 
7107837.4%
 
ValueCountFrequency (%) 
1301< 0.1%
 
872< 0.1%
 
831< 0.1%
 
744< 0.1%
 
721< 0.1%
 

WindSpeed3pm
Real number (ℝ≥0)

MISSING

Distinct44
Distinct (%)< 0.1%
Missing3062
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean18.66265678
Minimum0
Maximum87
Zeros1112
Zeros (%)0.8%
Memory size1.1 MiB
2022-12-26T16:13:47.498767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q113
median19
Q324
95-th percentile35
Maximum87
Range87
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.809800021
Coefficient of variation (CV)0.4720549773
Kurtosis0.7638582384
Mean18.66265678
Median Absolute Deviation (MAD)6
Skewness0.6282154194
Sum2657525
Variance77.61257641
MonotocityNot monotonic
2022-12-26T16:13:47.633774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%) 
13125808.6%
 
17125398.6%
 
20117138.1%
 
15114837.9%
 
19112637.7%
 
11100156.9%
 
997536.7%
 
2490526.2%
 
2285985.9%
 
2865534.5%
 
Other values (34)3884926.7%
 
ValueCountFrequency (%) 
011120.8%
 
210340.7%
 
422491.5%
 
638052.6%
 
759034.1%
 
ValueCountFrequency (%) 
871< 0.1%
 
832< 0.1%
 
781< 0.1%
 
762< 0.1%
 
741< 0.1%
 

Humidity9am
Real number (ℝ≥0)

MISSING

Distinct101
Distinct (%)0.1%
Missing2654
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean68.88083134
Minimum0
Maximum100
Zeros1
Zeros (%)< 0.1%
Memory size1.1 MiB
2022-12-26T16:13:47.790516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34
Q157
median70
Q383
95-th percentile98
Maximum100
Range100
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.02916445
Coefficient of variation (CV)0.2762621194
Kurtosis-0.03755504182
Mean68.88083134
Median Absolute Deviation (MAD)13
Skewness-0.4839689946
Sum9836596
Variance362.1090997
MonotocityNot monotonic
2022-12-26T16:13:47.947753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
9933912.3%
 
7030262.1%
 
6930232.1%
 
6530142.1%
 
6830112.1%
 
7129762.0%
 
6629732.0%
 
6729502.0%
 
7429172.0%
 
7229142.0%
 
Other values (91)11261177.4%
 
ValueCountFrequency (%) 
01< 0.1%
 
15< 0.1%
 
28< 0.1%
 
310< 0.1%
 
420< 0.1%
 
ValueCountFrequency (%) 
10028632.0%
 
9933912.3%
 
9820991.4%
 
9717891.2%
 
9616091.1%
 

Humidity3pm
Real number (ℝ≥0)

MISSING

Distinct101
Distinct (%)0.1%
Missing4507
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean51.53911588
Minimum0
Maximum100
Zeros4
Zeros (%)< 0.1%
Memory size1.1 MiB
2022-12-26T16:13:48.104954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17
Q137
median52
Q366
95-th percentile88
Maximum100
Range100
Interquartile range (IQR)29

Descriptive statistics

Standard deviation20.79590166
Coefficient of variation (CV)0.4034974466
Kurtosis-0.5113632484
Mean51.53911588
Median Absolute Deviation (MAD)14
Skewness0.03361436764
Sum7264593
Variance432.4695257
MonotocityNot monotonic
2022-12-26T16:13:48.261688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5227511.9%
 
5527381.9%
 
5727281.9%
 
5326971.9%
 
5926901.8%
 
5826431.8%
 
5426421.8%
 
5026241.8%
 
5126211.8%
 
6026151.8%
 
Other values (91)11420478.5%
 
(Missing)45073.1%
 
ValueCountFrequency (%) 
04< 0.1%
 
126< 0.1%
 
235< 0.1%
 
363< 0.1%
 
41130.1%
 
ValueCountFrequency (%) 
1004000.3%
 
994340.3%
 
986030.4%
 
974030.3%
 
964620.3%
 

Pressure9am
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct546
Distinct (%)0.4%
Missing15065
Missing (%)10.4%
Infinite0
Infinite (%)0.0%
Mean1017.64994
Minimum980.5
Maximum1041
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2022-12-26T16:13:48.465838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum980.5
5-th percentile1006.2
Q11012.9
median1017.6
Q31022.4
95-th percentile1029.5
Maximum1041
Range60.5
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation7.106530288
Coefficient of variation (CV)0.006983275888
Kurtosis0.2315626216
Mean1017.64994
Median Absolute Deviation (MAD)4.7
Skewness-0.09552363669
Sum132696463.9
Variance50.50277273
MonotocityNot monotonic
2022-12-26T16:13:48.638534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1016.48160.6%
 
1017.97890.5%
 
1016.37750.5%
 
1018.77750.5%
 
10187690.5%
 
1017.37690.5%
 
1015.97680.5%
 
1017.87660.5%
 
1017.27590.5%
 
1017.77590.5%
 
Other values (536)12265084.3%
 
(Missing)1506510.4%
 
ValueCountFrequency (%) 
980.51< 0.1%
 
9821< 0.1%
 
982.21< 0.1%
 
982.31< 0.1%
 
982.92< 0.1%
 
ValueCountFrequency (%) 
10411< 0.1%
 
1040.91< 0.1%
 
1040.62< 0.1%
 
1040.51< 0.1%
 
1040.43< 0.1%
 

Pressure3pm
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct549
Distinct (%)0.4%
Missing15028
Missing (%)10.3%
Infinite0
Infinite (%)0.0%
Mean1015.255889
Minimum977.1
Maximum1039.6
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2022-12-26T16:13:48.811173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum977.1
5-th percentile1004
Q11010.4
median1015.2
Q31020
95-th percentile1026.9
Maximum1039.6
Range62.5
Interquartile range (IQR)9.6

Descriptive statistics

Standard deviation7.037413808
Coefficient of variation (CV)0.006931665096
Kurtosis0.1291715572
Mean1015.255889
Median Absolute Deviation (MAD)4.8
Skewness-0.0456214048
Sum132421856.1
Variance49.52519311
MonotocityNot monotonic
2022-12-26T16:13:48.967796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1015.37860.5%
 
1015.57830.5%
 
1015.67760.5%
 
1015.77730.5%
 
1013.57670.5%
 
1015.17660.5%
 
1015.87650.5%
 
1015.47560.5%
 
10167470.5%
 
1014.87450.5%
 
Other values (539)12276884.4%
 
(Missing)1502810.3%
 
ValueCountFrequency (%) 
977.11< 0.1%
 
978.21< 0.1%
 
9791< 0.1%
 
980.22< 0.1%
 
981.21< 0.1%
 
ValueCountFrequency (%) 
1039.61< 0.1%
 
1038.91< 0.1%
 
1038.51< 0.1%
 
1038.41< 0.1%
 
1038.21< 0.1%
 

Cloud9am
Real number (ℝ≥0)

MISSING
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing55888
Missing (%)38.4%
Infinite0
Infinite (%)0.0%
Mean4.44746126
Minimum0
Maximum9
Zeros8642
Zeros (%)5.9%
Memory size1.1 MiB
2022-12-26T16:13:49.111282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q37
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.887158854
Coefficient of variation (CV)0.6491700961
Kurtosis-1.538830489
Mean4.44746126
Median Absolute Deviation (MAD)3
Skewness-0.2290818322
Sum398368
Variance8.335686245
MonotocityNot monotonic
2022-12-26T16:13:49.203804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
71997213.7%
 
11568710.8%
 
81469710.1%
 
086425.9%
 
681715.6%
 
265004.5%
 
359144.1%
 
555673.8%
 
444203.0%
 
92< 0.1%
 
(Missing)5588838.4%
 
ValueCountFrequency (%) 
086425.9%
 
11568710.8%
 
265004.5%
 
359144.1%
 
444203.0%
 
ValueCountFrequency (%) 
92< 0.1%
 
81469710.1%
 
71997213.7%
 
681715.6%
 
555673.8%
 

Cloud3pm
Real number (ℝ≥0)

MISSING
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing59358
Missing (%)40.8%
Infinite0
Infinite (%)0.0%
Mean4.509930083
Minimum0
Maximum9
Zeros4974
Zeros (%)3.4%
Memory size1.1 MiB
2022-12-26T16:13:49.313564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q37
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.72035731
Coefficient of variation (CV)0.6031927902
Kurtosis-1.456524516
Mean4.509930083
Median Absolute Deviation (MAD)2
Skewness-0.2263843461
Sum388314
Variance7.400343896
MonotocityNot monotonic
2022-12-26T16:13:49.407700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
71822912.5%
 
11497610.3%
 
8126608.7%
 
689786.2%
 
272265.0%
 
369214.8%
 
568154.7%
 
453223.7%
 
049743.4%
 
91< 0.1%
 
(Missing)5935840.8%
 
ValueCountFrequency (%) 
049743.4%
 
11497610.3%
 
272265.0%
 
369214.8%
 
453223.7%
 
ValueCountFrequency (%) 
91< 0.1%
 
8126608.7%
 
71822912.5%
 
689786.2%
 
568154.7%
 

Temp9am
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct441
Distinct (%)0.3%
Missing1767
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean16.99063142
Minimum-7.2
Maximum40.2
Zeros36
Zeros (%)< 0.1%
Memory size1.1 MiB
2022-12-26T16:13:49.533083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-7.2
5-th percentile6.9
Q112.3
median16.7
Q321.6
95-th percentile28.2
Maximum40.2
Range47.4
Interquartile range (IQR)9.3

Descriptive statistics

Standard deviation6.488753141
Coefficient of variation (CV)0.3819018247
Kurtosis-0.3405233369
Mean16.99063142
Median Absolute Deviation (MAD)4.6
Skewness0.0885399966
Sum2441434.8
Variance42.10391732
MonotocityNot monotonic
2022-12-26T16:13:49.674711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
179120.6%
 
13.89000.6%
 
14.88940.6%
 
168820.6%
 
148760.6%
 
158670.6%
 
16.68670.6%
 
16.58560.6%
 
138480.6%
 
15.18460.6%
 
Other values (431)13494592.8%
 
(Missing)17671.2%
 
ValueCountFrequency (%) 
-7.21< 0.1%
 
-71< 0.1%
 
-6.21< 0.1%
 
-5.91< 0.1%
 
-5.62< 0.1%
 
ValueCountFrequency (%) 
40.21< 0.1%
 
39.41< 0.1%
 
39.11< 0.1%
 
391< 0.1%
 
38.91< 0.1%
 

Temp3pm
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct502
Distinct (%)0.4%
Missing3609
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean21.68339032
Minimum-5.4
Maximum46.7
Zeros17
Zeros (%)< 0.1%
Memory size1.1 MiB
2022-12-26T16:13:49.831628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-5.4
5-th percentile11.6
Q116.6
median21.1
Q326.4
95-th percentile33.7
Maximum46.7
Range52.1
Interquartile range (IQR)9.8

Descriptive statistics

Standard deviation6.93665046
Coefficient of variation (CV)0.3199061751
Kurtosis-0.1362814705
Mean21.68339032
Median Absolute Deviation (MAD)4.8
Skewness0.237960364
Sum3075810.6
Variance48.1171196
MonotocityNot monotonic
2022-12-26T16:13:49.990104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
208820.6%
 
198690.6%
 
18.58690.6%
 
18.48680.6%
 
17.88590.6%
 
19.48400.6%
 
188390.6%
 
19.28390.6%
 
178340.6%
 
19.38330.6%
 
Other values (492)13331991.7%
 
(Missing)36092.5%
 
ValueCountFrequency (%) 
-5.41< 0.1%
 
-5.11< 0.1%
 
-4.41< 0.1%
 
-4.21< 0.1%
 
-4.11< 0.1%
 
ValueCountFrequency (%) 
46.71< 0.1%
 
46.21< 0.1%
 
46.13< 0.1%
 
45.91< 0.1%
 
45.82< 0.1%
 

RainToday
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing3261
Missing (%)2.2%
Memory size1.1 MiB
No
110319 
Yes
31880 
(Missing)
 
3261
ValueCountFrequency (%) 
No11031975.8%
 
Yes3188021.9%
 
(Missing)32612.2%
 
2022-12-26T16:13:50.083423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

RainTomorrow
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing3267
Missing (%)2.2%
Memory size1.1 MiB
No
110316 
Yes
31877 
(Missing)
 
3267
ValueCountFrequency (%) 
No11031675.8%
 
Yes3187721.9%
 
(Missing)32672.2%
 
2022-12-26T16:13:50.114351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Interactions

2022-12-26T16:12:55.782942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:12:55.930100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:12:56.071105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:12:56.212497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:12:56.353646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:12:56.510890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:12:56.651845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:12:56.793361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:12:56.950132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:12:57.091693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:12:57.232800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:12:57.390014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:12:57.531165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:12:57.672277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:12:57.813751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:12:57.970443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:12:58.096447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:12:58.237560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:12:58.362973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:12:58.489004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:12:58.630077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:12:58.771223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:12:58.897165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:12:59.039159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:12:59.180282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:12:59.321871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:12:59.447327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:12:59.572706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:12:59.714114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:12:59.855103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:12:59.991991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:00.137541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:00.263031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:00.393068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:00.530164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:00.655581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:00.797157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:00.938310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:01.063797image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:01.204832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:01.330384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:01.471439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:01.597390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:01.722924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:01.848411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:01.997554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:02.115526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:02.256629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:02.382110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:02.523696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:02.680423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:02.822007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:02.978168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:03.135266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:03.276354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:03.433543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:03.590286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:03.731868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:03.872970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:04.030228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:04.171345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:04.328464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:04.469569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:04.626816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:04.767807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:04.925056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:05.069246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:05.210757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:05.367535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:05.509060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:05.665813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:05.807371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:05.964143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:06.107270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:06.246856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:06.403592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:06.545220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:06.762727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:06.943737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:07.084851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:07.225935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:07.382525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:07.510944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:07.649479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:07.806169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:07.978942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:08.936758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:09.092822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:09.234391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:09.359899image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:09.501001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:09.642624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:09.815545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:09.956618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:10.082105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:10.223653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:10.349199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:10.516949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:10.647494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:10.788527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:10.945780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:11.102625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:11.228555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:11.385329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:11.526870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:11.667992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:11.809101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:11.950734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:12.091801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:12.233405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:12.374559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:12.551627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:12.677681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:12.834867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:12.975984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:13.117089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:13.258716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:13.399740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:13.541232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:13.697851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:13.839365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:13.980538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:14.125193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:14.263267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:14.419377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:14.560779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:14.717397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:14.858964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:15.000053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:15.141584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:15.267048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:15.392535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:15.534135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:15.675131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:15.816277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:15.957922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:16.083418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:16.229881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:16.554331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:16.695365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:16.831396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:16.962450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:17.087929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:17.231581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:17.370642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:17.511787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:17.653270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:17.778646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:17.919633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:18.045519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:18.186604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:18.327135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:18.453145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:18.594263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:18.735786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:18.876927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:19.017977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:19.159664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:19.300730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:19.442210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:19.598921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:19.865913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:20.085463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:20.273946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:20.540750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:20.760262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:21.042669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:21.355944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:21.983277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:22.342211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:22.720373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:22.971673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:23.112785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:23.253846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:23.395003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:23.536127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:23.677726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:23.803225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:23.944842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:24.070285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:24.226956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:24.430908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:24.556868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:24.713651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:24.870867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:25.027675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:25.184781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:25.404412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:25.577300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:25.718410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:25.953484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:26.173138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:26.314106image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:26.533732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:26.769417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:26.952314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:27.208750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:27.475693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:27.757622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:28.071364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:28.369425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:28.588945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:29.096778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:29.357585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:29.614359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:29.771542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:29.975694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:30.116827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:30.259022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:30.399564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:30.572233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:30.791310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:31.026529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:31.277730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:31.512979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:31.685767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:31.874106image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:32.078052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:32.282228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:32.464076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:32.658054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:32.784041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:32.972486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:33.113632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:33.239074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:33.411877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:33.569018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:33.694403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:33.867682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:34.086862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:34.243543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:34.368988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:34.557031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:34.698573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:34.886882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:35.075347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:35.216500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:35.436134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:35.672397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:35.891516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:36.158057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:36.283468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:36.424594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:36.549970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:36.707110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:36.863830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:37.005454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:37.146541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:37.272071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:37.413601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:37.570336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:37.711962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:37.837385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:37.994623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:38.120107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:38.261144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-12-26T16:13:50.192762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-26T16:13:50.413335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-26T16:13:50.633518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-26T16:13:50.869340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2022-12-26T16:13:51.108841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2022-12-26T16:13:39.003580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:40.182733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:42.251345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-26T16:13:43.051391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Sample

First rows

DateLocationMinTempMaxTempRainfallEvaporationSunshineWindGustDirWindGustSpeedWindDir9amWindDir3pmWindSpeed9amWindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRainTodayRainTomorrow
02008-12-01Albury13.422.90.6NaNNaNW44.0WWNW20.024.071.022.01007.71007.18.0NaN16.921.8NoNo
12008-12-02Albury7.425.10.0NaNNaNWNW44.0NNWWSW4.022.044.025.01010.61007.8NaNNaN17.224.3NoNo
22008-12-03Albury12.925.70.0NaNNaNWSW46.0WWSW19.026.038.030.01007.61008.7NaN2.021.023.2NoNo
32008-12-04Albury9.228.00.0NaNNaNNE24.0SEE11.09.045.016.01017.61012.8NaNNaN18.126.5NoNo
42008-12-05Albury17.532.31.0NaNNaNW41.0ENENW7.020.082.033.01010.81006.07.08.017.829.7NoNo
52008-12-06Albury14.629.70.2NaNNaNWNW56.0WW19.024.055.023.01009.21005.4NaNNaN20.628.9NoNo
62008-12-07Albury14.325.00.0NaNNaNW50.0SWW20.024.049.019.01009.61008.21.0NaN18.124.6NoNo
72008-12-08Albury7.726.70.0NaNNaNW35.0SSEW6.017.048.019.01013.41010.1NaNNaN16.325.5NoNo
82008-12-09Albury9.731.90.0NaNNaNNNW80.0SENW7.028.042.09.01008.91003.6NaNNaN18.330.2NoYes
92008-12-10Albury13.130.11.4NaNNaNW28.0SSSE15.011.058.027.01007.01005.7NaNNaN20.128.2YesNo

Last rows

DateLocationMinTempMaxTempRainfallEvaporationSunshineWindGustDirWindGustSpeedWindDir9amWindDir3pmWindSpeed9amWindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRainTodayRainTomorrow
1454502017-06-16Uluru5.224.30.0NaNNaNE24.0SEE11.011.053.024.01023.81020.0NaNNaN12.323.3NoNo
1454512017-06-17Uluru6.423.40.0NaNNaNESE31.0SESE15.017.053.025.01025.81023.0NaNNaN11.223.1NoNo
1454522017-06-18Uluru8.020.70.0NaNNaNESE41.0SEE19.026.056.032.01028.11024.3NaN7.011.620.0NoNo
1454532017-06-19Uluru7.420.60.0NaNNaNE35.0ESEE15.017.063.033.01027.21023.3NaNNaN11.020.3NoNo
1454542017-06-20Uluru3.521.80.0NaNNaNE31.0ESEE15.013.059.027.01024.71021.2NaNNaN9.420.9NoNo
1454552017-06-21Uluru2.823.40.0NaNNaNE31.0SEENE13.011.051.024.01024.61020.3NaNNaN10.122.4NoNo
1454562017-06-22Uluru3.625.30.0NaNNaNNNW22.0SEN13.09.056.021.01023.51019.1NaNNaN10.924.5NoNo
1454572017-06-23Uluru5.426.90.0NaNNaNN37.0SEWNW9.09.053.024.01021.01016.8NaNNaN12.526.1NoNo
1454582017-06-24Uluru7.827.00.0NaNNaNSE28.0SSEN13.07.051.024.01019.41016.53.02.015.126.0NoNo
1454592017-06-25Uluru14.9NaN0.0NaNNaNNaNNaNESEESE17.017.062.036.01020.21017.98.08.015.020.9NoNaN